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PubMed2026

Phylotranscriptomics Allows Distinguishing Major Gene Flow Events from Incomplete Lineage Sorting in Rapidly Diversifying Mimetic Orchids (Genus Ophrys).

Ophrys orchids (or bee orchids) provide an outstanding example of a plant adaptive radiation. Over the last 5 million years, this genus has diversified into hundreds of taxa as a result of its unconventional pollination strategy, known as "sexual swindling". However, the rapid and substantial diversification of this genus, combined with its capacity for hybridization and large genome size, poses significant challenges in addressing its systematics. We used phylotranscriptomics as a genome complexity reduction technique to infer the phylogenetic relationships among Ophrys main lineages. More than seven thousand gene trees enabled us to determine the relative contributions of gene flow and incomplete lineage sorting (ILS) in Ophrys evolution. First, we propose a new phylogenetic hypothesis for the genus with an unprecedented resolution that largely confirms the relationships between the main Ophrys lineages, but also provides new insights within each subgenera. By combining phylogenetic network inference with introgression analyzes based on gene tree topologies and branch lengths, we then show that the numerous phylogenetic incongruences among gene tree topologies result from a pervasive background of ILS, over which stand out several well-supported, ancient and potentially adaptive gene flow events between lineages. These major gene flow events provide a new perspective on the evolution of the Ophrys genus and its pollination, questioning previous hypotheses inferred without considering its reticulate evolution, and providing a better understanding of discrepancies observed among previous phylogenetic studies of the genus.

Orchidaceae↗

PubMed2002

Automatic detection of distorted plethysmogram pulses in neonates and paediatric patients using an adaptive-network-based fuzzy inference system.

Despite the fact that pulse oximetry has become an essential technology in respiratory monitoring of neonates and paediatric patients, it is still fraught with artefacts causing false alarms resulting from patient or probe movement. As the shape of the plethysmogram has always been considered as a useful visual indicator for determining the reliability of SaO(2) numerical readings, automation of this observation might benefit health care providers at the bedside. We observed that the systolic upstroke time (t(1)), the diastolic time (t(2)) and heart rate (HR) extracted from the plethysmogram pulse constitute features, which can be used for detecting normal and distorted plethysmogram pulses. We developed a technique for classifying plethysmogram pulses into two categories: valid and artefact via implementations of fuzzy inference systems (FIS), which were tuned using an adaptive-network-based fuzzy inference system (ANFIS) and receiver operating characteristics (ROC) curves analysis. Features extracted from a total of 22,497 pulse waveforms obtained from 13 patients were used to systematically optimise the FIS. A further 2843 waveforms obtained from another eight patients were used for testing the system, and visually classified into 1635 (58%) valid and 1208 (42%) distorted segments. For the optimum system, the area under the ROC curve was 0.92. The system was able to classify 1418 (87%) valid segments and 897 (74%) distorted segments correctly. The calculations of the system's performance showed 87% sensitivity, 81% accuracy and 74% specificity. In comparison with the 95% confidence interval (CI) thresholding method, the fuzzy system showed higher specificity (P=0.008,P<0.01), and no significant difference was found between the two methods in terms of sensitivity (P=0.720,P>0.05) and accuracy (P=0.053,P>0.05). We therefore conclude that the algorithm used in this system has some potential in detecting valid and distorted plethysmogram pulse. However, further evaluation is needed using larger patient groups.

Adolescent↗

PubMed2005

Least absolute regression network analysis of the murine osteoblast differentiation network.

MOTIVATION: We propose a reverse engineering scheme to discover genetic regulation from genome-wide transcription data that monitors the dynamic transcriptional response after a change in cellular environment. The interaction network is estimated by solving a linear model using simultaneous shrinking of the least absolute weights and the prediction error. RESULTS: The proposed scheme has been applied to the murine C2C12 cell-line stimulated to undergo osteoblast differentiation. Results show that our method discovers genetic interactions that display significant enrichment of co-citation in literature. More detailed study showed that the inferred network exhibits properties and hypotheses that are consistent with current biological knowledge.

Animals↗

PubMed2005

ASIAN: a web server for inferring a regulatory network framework from gene expression profiles.

The standard workflow in gene expression profile analysis to identify gene function is the clustering by various metrics and techniques, and the following analyses, such as sequence analyses of upstream regions. A further challenging analysis is the inference of a gene regulatory network, and some computational methods have been intensively developed to deduce the gene regulatory network. Here, we describe our web server for inferring a framework of regulatory networks from a large number of gene expression profiles, based on graphical Gaussian modeling (GGM) in combination with hierarchical clustering (http://eureka.ims.u-tokyo.ac.jp/asian). GGM is based on a simple mathematical structure, which is the calculation of the inverse of the correlation coefficient matrix between variables, and therefore, our server can analyze a wide variety of data within a reasonable computational time. The server allows users to input the expression profiles, and it outputs the dendrogram of genes by several hierarchical clustering techniques, the cluster number estimated by a stopping rule for hierarchical clustering and the network between the clusters by GGM, with the respective graphical presentations. Thus, the ASIAN (Automatic System for Inferring A Network) web server provides an initial basis for inferring regulatory relationships, in that the clustering serves as the first step toward identifying the gene function.

Cluster Analysis↗

PubMed2005

Informative structure priors: joint learning of dynamic regulatory networks from multiple types of data.

We present a method for jointly learning dynamic models of transcriptional regulatory networks from gene expression data and transcription factor binding location data. Models are automatically learned using dynamic Bayesian network inference algorithms; joint learning is accomplished by incorporating evidence from gene expression data through the likelihood, and from transcription factor binding location data through the prior. We propose a new informative structure prior with two advantages. First, the prior incorporates evidence from location data probabilistically, allowing it to be weighed against evidence from expression data. Second, the prior takes on a factorable form that is computationally efficient when learning dynamic regulatory networks. Results obtained from both simulated and experimental data from the yeast cell cycle demonstrate that this joint learning algorithm can recover dynamic regulatory networks from multiple types of data that are more accurate than those recovered from each type of data in isolation.

Bayes Theorem↗

PubMed2025

Identification of a PRDM1-regulated T cell network to regulate atherosclerotic plaque inflammation.

BACKGROUND: Inflammation is a key driver of atherosclerosis, yet the mechanisms sustaining inflammation in human plaques remain poorly understood. This study uses a network-based approach to identify immune gene programs involved in the transition from low- to high-risk (rupture-prone) human atherosclerotic plaques. METHODS: Expression data from human carotid artery plaques, both stable (low-risk, n = 16) and unstable (high-risk, n = 27), were analyzed using Weighted Gene Co-expression Network Analysis (WGCNA). Bayesian network inference, operated on the eigengene values from the WGCNA, further extended the WGCNA analysis, and similarity to the signature of T cell subsets was validated in single-cell RNA sequencing data of human plaques, and a loss-of-function study in a mouse model of atherosclerosis. In silico drug repurposing was performed to identify potential therapeutic targets. RESULTS: Our analysis revealed a distinct gene module with a prominent T cell signature, particularly in unstable plaques. Key regulatory factors, RUNX3, IRF7 and in particular PRDM1, were significantly downregulated in plaque T cells from symptomatic versus asymptomatic patients, indicating a protective role. Additionally, as PRDM1 is downstream of IRF7, we opted for PRDM1 as a key target. T cell-specific Prdm1 deficiency in Western-type diet fed Ldlr knockout mice featured accelerated plaque progression. Finally, as PRDM1 targeting drugs are not yet available, we performed in silico drug repurposing, identifying EGFR inhibitors as promising therapeutic candidates. CONCLUSIONS: This study highlights a PRDM1-regulated T cell network that distinguishes high-risk from low-risk plaques and demonstrates the regulatory role of T cell PRDM1 in controlling atherosclerosis, positioning this pathway as a promising therapeutic target.

Plaque, Atherosclerotic↗

PubMedDate not supplied

GenePath: a system for inference of genetic networks and proposal of genetic experiments.

A genetic network is a formalism that is often used in biology to represent causalities and reason about biological phenomena related to genetic regulation. We present GenePath, a computer-based system that supports the inference of genetic networks from a set of genetic experiments. Implemented in Prolog, GenePath uses abductive inference to elucidate network constraints based on background knowledge and experimental results. Additionally, it can propose genetic experiments that may further refine the discovered network and establish relations between genes that could not be related based on the original experimental data. We illustrate GenePath's approach and utility on analysis of data on aggregation and sporulation of the soil amoeba Dictyostelium discoideum.

Animals↗

PubMed2026

A chromatin-informed transcriptional regulatory framework to stratify patients and guide therapy selection in triple-negative breast cancer.

Triple-negative breast cancer is an aggressive and heterogeneous breast cancer subtype with few effective targeted therapies and frequent resistance to chemotherapy. Here, we integrate transcriptional regulatory network inference with chromatin accessibility across a large-scale multi-system collection of primary tumors, patient-derived xenografts and model cell lines to quantify transcription factor activity and identify regulators that underpin triple-negative breast cancer identity. This approach prioritizes 94 high-confidence triple-negative breast cancer transcription factors whose activity capture inter-tumor heterogeneity and independently stratify patient outcome across clinical endpoints. Linking transcription factor activity to pharmacogenomic drug sensitivity profiles identifies reproducible drug-transcription factor associations across independent datasets, including NFE2L3 and CBFB activity as predictors of sensitivity to mTOR inhibition, which we validate in everolimus-treated triple-negative breast cancer patient-derived xenograft models. Collectively, we provide a transcriptional and chromatin-informed framework to capture triple-negative breast cancer regulatory state and expand transcription factor guided precision medicine to this breast cancer subtype.

Humans↗

PubMed2026

Livestock Multi-Omics Integration: A Systematic Framework From Statistical Association to Causal Interpretation.

Livestock multi-omics integration is key to unraveling complex trait regulation, yet systematic, livestock-specific strategies remain scarce. This review traces the progression from single-omics accumulation to multi-dimensional integration, highlighting how large-scale genomic, epigenomic, and transcriptomic projects lay the foundation for functional dissection. We identify core impediments: extreme species diversity, marked data heterogeneity, limited sample sizes, and a pervasive reduction of multi-omics data to simplistic differential screens, resulting in low translational efficiency. We critically appraise four common pitfalls-overinterpreting correlation as causation, relegating proteomics to corroborating transcriptomics, incomplete microbiome-host integration lacking environmental context, and systematic neglect of metabolic fluxomics-and show how exposomics and fluxomics add necessary causal and dynamic dimensions. To address these, we propose a livestock-adapted three-tier analytical framework: (1) statistical association of cross-omics covariation patterns; (2) machine learning-driven feature mining and integrative modeling; and (3) causal interpretation encompassing Mendelian randomization, prior-knowledge-guided network inference, and physical causal evidence via fluxomics and metabolic control analysis. We further discuss how multimodal sequencing (single-cell, spatial, temporal) and generative AI can fundamentally mitigate heterogeneity and strengthen causal evidence. Finally, we outline future priorities in database standardization, livestock-specific benchmarking, and translational pipelines, charting a path from correlation-centric reporting to mechanistic causality and precision breeding.

Animals↗

PubMed2005

Phenotype analysis using network motifs derived from changes in regulatory network dynamics.

The intrinsic dynamic response of a transcriptional regulatory network depends directly on molecular interactions in the cellular transcription, translation, and degradation machineries. These interactions can be incorporated into dynamic mathematical models of the biochemical system using the biophysical relationship with the model parameters. Modifications of such interactions bring changes to the biological behavior of the cells, and therefore, many normal and pathological cellular states depend on them. It is important for analysis, prediction, diagnosis, and treatment of cellular function to have an experimentally derived model with parameters that adequately represent the molecular interactions of interest. Finding the model and parameters of a transcriptional regulatory network is a difficult task that has been approached at different levels and with different techniques. We develop here a new analysis method (based on previous work on network inference, modeling, and parameter identification) that finds the most changed parameters from yeast oligonucleotide microarray expression patterns in cases where a phenotype difference exists between two samples. We then relate and examine the changed parameters with their associated genes, corresponding genetic functional categories, and particular subnetworks and connectivities. The biophysical bases for these changes are also identified by studying the relationship of the changed parameters with the transcription, translation, and degradation mechanisms. The method is improved to cases where there are two or more transcription factors influencing transcription, and a statistical analysis is performed to give a measurement of the uniqueness and robustness of the parameter fit.

Algorithms↗

PubMed1996

On the interpretation of certainty factors in expert systems.

Despite the strong theoretical foundation the Bayesian probabilistic approach to model uncertainty in medicine meets many difficulties at the implementation step. One of these difficulties is related to a large amount of conditional probabilities to be assessed and in many cases this task was recognised to be practically insoluble. The MYCIN certainty factors model is a widely distributed pragmatical approach for modeling reasoning under uncertainty that substantially simplifies the problem, at the sacrifice of theoretical soundness. One can determine certainty factors as a function of prior and posterior probability. However, this approach is only consistent with the modularity axiom for certainty factors for tree-structure inference networks, which is rarely true for practical applications. In this paper we abandon the requirement of a direct probabilistic interpretation of certainty factors and build a model of propagation of uncertainty in terms of absolute belief and belief updates. We describe our model for propagating uncertainty in terms of matrix multiplication with specifically defined addition and multiplication which correspond to parallel and sequential combinations of certainty factors. It is possible to define these operations in such a manner that they form a field, and therefore to obtain some useful properties. Finally we present a method of determining certainty factors from statistical data using nonlinear regression and illustrate it with a leukemia diagnostics problem.

Artificial Intelligence↗

PubMed2026

The opioid receptor-ligand network in human cancers: pan-cancer multi-omics profiling and translational implications.

BACKGROUND: Opioid receptor-ligand signalling has been implicated in tumour biology and perioperative outcomes; however, its pan-cancer molecular landscape and clinical relevance remain incompletely defined. METHODS: We performed a pan-cancer multi-omics analysis of eight predefined opioid receptor-ligand genes across 33 tumour types from The Cancer Genome Atlas. Analyses included gene expression analysis using the linear models for microarray data (limma) package, genomic alterations, DNA methylation, regulatory network inference, pathway activity estimation using gene set variation analysis, and survival modelling. Multivariable Cox regression models were adjusted for age, sex, and tumour stage. RESULTS: Opioid receptor-ligand genes exhibited heterogeneous and generally low-to-moderate expression across tumour types. Genomic and epigenetic alterations were tumour-specific and variably associated with gene expression. Selected genes showed associations with overall survival in a tumour-dependent manner; however, these associations were attenuated after adjustment for clinical covariates and were accompanied by wide confidence intervals in some cohorts. Pathway analyses suggested associations with broader biological programmes, including epithelial-mesenchymal transition and immune-related pathways. Regulatory analyses identified candidate transcription factors and miRNAs, although these findings are exploratory. CONCLUSIONS: This pan-cancer analysis provides a systematic overview of opioid receptor-ligand gene features across human cancers. The observed associations are context-dependent and should be interpreted as hypothesis-generating. Further mechanistic and prospective studies are required to determine the clinical relevance of opioid signalling in cancer and perioperative settings.

Humans↗

PubMed2003

Computational methods of analysis of protein-protein interactions.

Computational methods play an important role at all stages of the process of determining protein-protein interactions. They are used to predict potential interactions, to validate the results of high-throughput interaction screens and to analyze the protein networks inferred from interaction databases.

Computational Biology↗

PubMed2001

International variation in the interpretation of renal transplant biopsies: report of the CERTPAP Project.

BACKGROUND: The Banff working formulation of renal transplant pathology is intended to have international application. There remains a need to develop methods to harmonize the application of such grading systems between laboratories. Banff grades do not always permit precise management decisions to be made. Alternative schemes have been devised for the diagnosis of acute rejection, but there have been no independent tests of the different approaches. METHODS: Sections from 55 renal transplant biopsies were circulated around the laboratories of 22 major transplant units for the Convergence of European Renal Transplant Pathology Assessment Procedures (CERTPAP) Project. Participating pathologists were asked to grade 32 different histological features, without any clinical information. After each circulation of five cases, feedback was provided to participants. Statistical evidence of improvement in interobserver variation was sought. At the end of the study, correlations with the original clinicopathological diagnosis were sought. RESULTS: Interobserver variation was greater than has previously been reported. For every feature studied, some pathologists consistently under-grade or over-grade. There was relatively little evidence of improvement in interobserver variation as a result of the feedback system. No single feature permitted a reliable diagnosis of acute rejection. Applying the Banff and CCTT schemas to the histological grades showed no clear diagnostic advantage for either system, but a simple computer-based inference network, which combined data from 12 histological features, out performed either approach. Within the "protocol" biopsies studied, long-term survival correlated better with "acute" than with "chronic" histological features. CONCLUSIONS: These results do not undermine the value of the Banff classification, but they demonstrate a need for caution when translating biopsy results between institutions. It is obvious that evaluation of biopsies in multicenter trials must be done in one center. In the management of individual patients, the need to interpret Banff grades in the light of local experience and clinical information is stressed.

Biopsy↗

PubMed2006

Graph-based methods for analysing networks in cell biology.

Availability of large-scale experimental data for cell biology is enabling computational methods to systematically model the behaviour of cellular networks. This review surveys the recent advances in the field of graph-driven methods for analysing complex cellular networks. The methods are outlined on three levels of increasing complexity, ranging from methods that can characterize global or local structural properties of networks to methods that can detect groups of interconnected nodes, called motifs or clusters, potentially involved in common elementary biological functions. We also briefly summarize recent approaches to data integration and network inference through graph-based formalisms. Finally, we highlight some challenges in the field and offer our personal view of the key future trends and developments in graph-based analysis of large-scale datasets.

Artificial Intelligence↗

PubMed2026

Accelerated long-read variant calling with Clair3 for whole-genome sequencing.

SUMMARY: The rapid growth of genomic data and increasing adoption of long-read sequencing technologies have rendered variant calling one of the most computationally demanding tasks in genomic analysis. Although deep learning-based methods currently outperform conventional approaches in distinguishing true variants from complex sequencing noise, they impose prohibitive computational and time requirements. To address this limitation, we present a computational framework based on Clair3 that integrates parallelized feature generation, enhanced variant phasing, in-memory read haplotagging, and GPU-accelerated neural network inference to accelerate variant calling. By dynamically optimizing the use of both GPU and CPU resources, our method achieves substantial runtime improvements without compromising accuracy. We evaluated our framework across a range of sequencing depths, diverse samples, and multiple hardware configurations. Our results demonstrate that the optimized pipeline completes variant calling for a 30× whole-genome sequence in 12-20 minutes using standard computational resources (32 CPU threads and one NVIDIA GPU), and in 12-15 minutes on an Apple Mac Studio (32 threads), which is ∼10-20-fold speedup compared with its initial release. In addition to exceptional efficiency, our method maintains state-of-the-art accuracy, achieving SNP F1-scores of 99.32% and 99.70% on 30× ONT and PacBio GIAB HG003 datasets, respectively. This work introduces a rapid, accurate, and scalable variant calling framework that effectively supports large-cohort genomic studies and time-sensitive clinical applications. AVAILABILITY AND IMPLEMENTATION: The accelerated implementation of Clair3 is open source and available at: https://github.com/HKU-BAL/Clair3/tree/gpu.

Whole Genome Sequencing↗

PubMed2003

CLICK and EXPANDER: a system for clustering and visualizing gene expression data.

MOTIVATION: Microarrays have become a central tool in biological research. Their applications range from functional annotation to tissue classification and genetic network inference. A key step in the analysis of gene expression data is the identification of groups of genes that manifest similar expression patterns. This translates to the algorithmic problem of clustering genes based on their expression patterns. RESULTS: We present a novel clustering algorithm, called CLICK, and its applications to gene expression analysis. The algorithm utilizes graph-theoretic and statistical techniques to identify tight groups (kernels) of highly similar elements, which are likely to belong to the same true cluster. Several heuristic procedures are then used to expand the kernels into the full clusters. We report on the application of CLICK to a variety of gene expression data sets. In all those applications it outperformed extant algorithms according to several common figures of merit. We also point out that CLICK can be successfully used for the identification of common regulatory motifs in the upstream regions of co-regulated genes. Furthermore, we demonstrate how CLICK can be used to accurately classify tissue samples into disease types, based on their expression profiles. Finally, we present a new java-based graphical tool, called EXPANDER, for gene expression analysis and visualization, which incorporates CLICK and several other popular clustering algorithms. AVAILABILITY: http://www.cs.tau.ac.il/~rshamir/expander/expander.html

Algorithms↗

PubMed2006

A framework of integrating gene relations from heterogeneous data sources: an experiment on Arabidopsis thaliana.

One of the most important goals of biological investigation is to uncover gene functional relations. In this study we propose a framework for extraction and integration of gene functional relations from diverse biological data sources, including gene expression data, biological literature and genomic sequence information. We introduce a two-layered Bayesian network approach to integrate relations from multiple sources into a genome-wide functional network. An experimental study was conducted on a test-bed of Arabidopsis thaliana. Evaluation of the integrated network demonstrated that relation integration could improve the reliability of relations by combining evidence from different data sources. Domain expert judgments on the gene functional clusters in the network confirmed the validity of our approach for relation integration and network inference.

Arabidopsis↗